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<h1>When perl is not quite fast enough</h1>
<div class='content'>
<p>Category: <a href='Translation.html'>Translation</a> &nbsp; Keywords: <b>faster</b></p>Original URL:<a href="http://www.ccl4.org/~nick/P/Fast_Enough/">http://www.ccl4.org/~nick/P/Fast_Enough/</a><br>
<Author: Nicholas Clark><br>
<Translated by: Fayland @ www.1313s.com><br>
<UL>
  <LI><A href="#when_perl_is_not_quite_fast_enough">When perl is not quite fast enough</A> 
  <LI>
  <UL>
    <LI><A href="#introduction">简介</A> 
    <LI><A href="#obvious_things">一些明显的事</A> 
    <LI><A href="#compromises">某些折衷/Compromises</A> 
    <LI><A href="#banish_the_demons_of_stupidity">放逐愚蠢的魔鬼/Banish the demons of stupidity</A> 
    <LI><A href="#intermission">间断</A> 
    <LI><A href="#tests">测试</A> 
    <LI><A href="#what_causes_slowness">What causes slowness</A> 
    <LI><A href="#step_by_step">Step by step</A> 
    <LI><A href="#small_easy_things">简单的小事</A> 
    <LI><A href="#needless_importing_is_slow">无关的倒入会变慢</A> 
    <LI><A href="#regexps">regexps</A> 
    <LI><A href="#devel::dprof">Devel::DProf</A> 
    <LI><A href="#benchmark">Benchmark</A> 
    <LI><A href="#what_causes_slowness_in_perl">What causes slowness in perl?</A> 
    <LI><A href="#ops_are_bad,_m'kay1">Ops are bad, m'kay</A> 
    <LI><A href="#memoize">Memoize</A> 
    <LI><A href="#miscellaneous">Miscellaneous</A> 
    <LI><A href="#yay_for_y">yay for y</A> 
    <LI><A href="#ops_are_bad,_m'kay2">Ops are bad, m'kay</A> 
    <LI><A href="#how_to_make_perl_fast_enough">How to make perl fast enough</A> </LI></UL></LI></UL>

<h2><a name=when_perl_is_not_quite_fast_enough>When perl is not quite fast enough</a></h2>
<P>此份手稿是为了我在 YAPC::EU::2002 上的讲演而准备的。它不是我实际上所说讲演的抄本，更合适的说法是，它只是转成幻灯片的 pod 笔记，一些随手写在纸上或存在脑中的笔记，我试着将它们做成连贯的文章。我还试着增加一些有用的反馈 - 有时我能记起这是谁说的，在此感谢他们。</P>
<P><A href="http://www.ccl4.org/~nick/P/Fast_Enough/00.html">您可以按此查看幻灯片</A>，我希望找到幻灯片做了哪些改变是显而易见的。</P>
<h2><a name=introduction>简介</a></h2>
<P>你可能有些 Perl 程序，但是它们运行得很慢。然后你很想对此做些改变。此文讲述了怎样对程序提速和怎样及时地避免问题。</P>
<h2><a name=obvious_things>一些明显的事</a></h2>
<DL>
  <DT><a name=item_find_better_algorithm>寻找更好的算法</A> 
  <DD>您的程序运行在您所想的最有效的方法之上。但是很有可能别人从另一个完全不同的角度看问题，从而找到一个快 100 倍的算法。您<EM>确定</EM>你使用了最好的算法？做些研究吧。
  <DT><A name=item_throw_more_hardware_at_it>使用更好的硬件</A> 
  <DD>如果您的程序不是在许多台机子上运行，或许使用更好的硬件会比较便宜。毕竟硬件是越来越便宜而程序员要付高工资。或许你能通过改变硬件而获得高性能；也有可能给您的机子编译一个自定义内核就足够了。
  <DT><A name=item_mod_perl>mod_perl</A> 
  <DD>对于我所写的 CGI 程序，我发现即使我削减了所有可以削减的，服务器还是只能每秒供应 2.5 。而运行在同一机子上同样的代码，不同的是使用了 mod_perl ，它能每秒供应 25 。这是一个不费很大力气就能做 10 倍提速的方法。如果您的代码不适合于运行在 mod_perl 下，这里还有 <A href="http://www.fastcgi.com/">fastcgi</A> (CGI.pm 支持)。如果您的程序不是 CGI ，可以考虑持续性的 perl daemon，见 <A href="http://search.cpan.org/search?mode=module&query=PPerl">PPerl on CPAN</A>。
  <DT><A name=item_rewrite_in_c%2c_er_c%2b%2b%2c_sorry_java%2c_i_mean>重写于 <STRONG>C</STRONG>, 或 <STRONG>C++</STRONG>, sorry <STRONG>Java</STRONG>, I mean <STRONG>C#</STRONG>, oops no ...</A> 
  <DD>当然，最后一个“显而易见”的解决方案就是用 native 语言重写您的代码，如 C, C++, Java, C# 或者任何当前合适的。</DD></DL>
<P>但是这些可能不是实际或策略上可接受的方案。</P>
<h2><A name=compromises>某些折衷</a></h2>
<P>那么，你能做某些折衷。</P>
<DL>
  <DT><a name=item_xs>XS</A> 
  <DD>您可能发现 5% 的代码使用了 95% 的时间，这些时间 Perl 用来做它低效的工作，如移动位/bit shifting。因此你能用 C 来写那 bit ，用 Perl 写剩下的，最后用 XS 将二者联合起来。但是你需要学习 XS 和 perl API，这是份艰巨的工作。
  <DT><A name=item_inline>Inline</A> 
  <DD>或许你能使用 <A href="http://search.cpan.org/search?mode=module&query=Inline::C">Inline</A>。如果你要操作 Perl 内部，你仍然需要学习 perl API，但是如果你只需要在您的纯 C 代码或他人的 C 库中调用 perl，Inline 让此轻而易举。
  <P>如下是我的 perl 程序，它调用了 perl 函数 <code>rot32</code>。而这里是一个 C 函数 <code>rot32</code> ，它接受两个整数，用第二个整数旋转第一个，然后返回一个整数值。这是你所需的，它能工作得很好。</P><PRE>    #!/usr/local/bin/perl -w
    use strict;
    
    printf "$_:\t%08X\t%08X\n", rot32 (0xdead, $_), rot32 (0xbeef, -$_)
      foreach (0..31);
    
    use Inline C => <<'EOC';
    
    unsigned rot32 (unsigned val, int by) {
      if (by >= 0)
        return (val >> by) | (val << (32 - by));
      return (val << -by) | (val >> (32 + by));
    }
    EOC
    __END__</PRE><PRE>    0:      0000DEAD        0000BEEF
    1:      80006F56        00017DDE
    2:      400037AB        0002FBBC
    3:      A0001BD5        0005F778
    4:      D0000DEA        000BEEF0
    ...</PRE>
  <DT><A name=item_compile_your_own_perl%3f>编译您自己的 perl ?</A> 
  <DD>您是否用系统提供的 perl 运行程序。编译自己的 perl 能让程序跑得更快。例如，当线程编译进 perl ，所有它的内部变量都要满足线程安全/thread safe，这能让程序变慢一点。如果 perl 可以使用线程，但你不使用线程，于是你毫无理由地付出了一点速度。同样地，你能有个比系统使用的更好的编译器。例如，我发现使用 gcc 3.2 比 2.9.5 某些我的 C 代码快了 5% 。[某个对我有益的激烈质问者/hecklers说他提升了 14% ，(如果我记忆无误的话)这是从重编译 perl 编译器所获得的] 
  <DT><A name=item_different_perl_version%3f>不同的 perl 版本?</A> 
  <DD>试着采用不同的 perl 版本。不同的版本对某些事情有提速。如果你使用了一个旧的 perl ，试着用最新版本。如果你运行最新版本而没使用这些最新的特性，那试着使用一个旧版本。</DD></DL>
<h2><A name=banish_the_demons_of_stupidity>放逐愚蠢的魔鬼/Banish the demons of stupidity</a></h2>
<P>您是否使用了这种语言最棒的特性？</P>
<DL>
  <DT><a name=item_hashes>hashes</A> 
  <DD>引用 Larry Wall 的话 - 在关联数组中使用线性搜索就像试着联合某人对付 loaded Uzi 。 
  <P>我相信你不会这么做。但是你是否保留您的数组已排序，这样你能使用对分检索？这很快。但是使用 hash 应该会更快。</P>
  <DT><A name=item_regexps>regexps</A> 
  <DD>没有正则表达式/regexps的语言需要你另外写代码来解析字符串。perl 有 regexps ，又它们重写或许能获得 10 倍加速。甚至使用锚标<code>\G</code>和标记<code>/gc</code>可能会更快。 <PRE>    if ( /\G.../gc ) {
        ...
    } elsif ( /\G.../gc ) {
        ...
    } elsif ( /\G.../gc ) {</PRE>
  <DT><A name=item_pack_and_unpack><code>pack</code> 和 <code>unpack</code></A> 

  <DD>pack 和 unpack 有太多的特性值得去记住。翻看 manpage - 你可能只用一个 unpack 代替整个子程序。
  <DT><A name=item_undef><code>undef</code></A> 
  <DD>undef. 什么是我意味的 undef? 
  <P>您是否只为了丢弃它而计算？</P>
  <P>For example the script in the Encode module that compiles character 
  conversion tables would print out a warning if it saw the same character 
  twice. If you or I build perl we'll just let those build warnings scroll off 
  the screen - we don't care - we can't do anything about it. And it turned out 
  that keeping track of everything needed to generate those warnings was slowing 
  things down considerably. So I added a flag to disable that code, and perl 5.8 
  defaults to use it, so it builds more quickly.</P></DD></DL>
<h2><A name=intermission>间断</a></h2>
<P>许多对我有益的激烈质问者/hecklers （大部分是看过讲演的 London.pm (我将 David Adler 做为 London.pm 的一分子因为他订阅了这份列表)）希望我提醒各位<STRONG>除非你确实绝对的需要，否则真的不应该去优化</STRONG>。您在让您的代码更难维修，更难扩展和更易引进新的 bugs 。很可能在一开始你就在需要优化的地方做了些不该做的事。</P>
<P>我同意上述观点。</P>
<P>同样，我也不想改变幻灯片的运行顺序。There isn't a 
good order to try to describe things in, and some of the ideas that follow are 
actually more "good practice" than optimisation techniques, so possibly ought to 
come before the slides on finding slowness. I'll mark what I think are good 
habits to get into, and once you understand the techniques then I'd hope that 
you'd use them automatically when you first write code. That way (hopefully) 
your code will never be so slow that you actually want to do some of the brute 
force optimising I describe here.</P>
<h2><a name=tests>测试</a></h2>
<DL>
  <DT><a name=item_must_not_introduce_new_bugs>必须不引进新的 bugs</A> 
  <DD>当你优化现有<STRONG>运行</STRONG>代码最重要的是不引进新的 bugs 。
  <DT><A name=item_use_your_full_regression_tests_%3a%2d%29>使用您所有的回归/regression测试 :-)</A> 
  <DD>尽可能使用您所有的一整套 regression 测试。您应该有，对不？
  <P>[这时候你们或许会不安地笑，因为我打赌只有非常少的人能写份全面的测试代码]</P>
  <DT><A name=item_keep_a_copy_of_original_program><STRONG>保留</STRONG>原始程序的备份</A> 
  <DD>您<STRONG>应该</STRONG>保留一份原始代码的拷贝。如果其他的都失败了这是您最后的凭借。试着使用版本控制系统/CSV。做一个脱机备份。检查您的备份是否可读。你得保证不能丢失它。
  <BR>最后，您对于优化时是否引进了新的 bugs 的最终测试为检查优化后的版本是否与原先版本有同样的输出结果。（当然，优化版本花费更少的时间）。
</DD></DL>
<h2><A name=what_causes_slowness>What causes slowness</a></h2>
<DL>
  <DT><a name=item_cpu>CPU</A> 
  <DD>It's obvious that if you script hogs the CPU for 10 seconds solid, then to 
  make it go faster you'll need to reduce the CPU demand. 
  <DT><A name=item_ram>RAM</A> 
  <DD>A lesser cause of slowness is memory. 
  <DL>
    <DT><A name=item_perl_trades_ram_for_speed>perl trades RAM for speed</A> 
    <DD>One of the design decisions Larry made for perl was to trade memory for 
    speed, choosing algorithms that use more memory to run faster. So perl tends 
    to use more memory. 
    <DT><A name=item_slower>getting slower (relative to CPU)</A> 
    <DD>CPUs keep getting faster. Memory is getting faster too. But not as 
    quickly. So in relative terms memory is getting slower. [Larry was correct 
    to choose to use more memory when he wrote perl5 over 10 years ago. However, 
    in the future CPU speed will continue to diverge from RAM speed, so it might 
    be an idea to revisit some of the CPU/RAM design trade offs in parrot] 
    <DT><A name=item_memory_like_a_pyramid>memory like a pyramid</A> 
    <DD>
    <P>You can never have enough memory, and it's never fast enough.</P>
    <P>Computer memory is like a pyramid. At the point you have the CPU and its 
    registers, which are very small and very fast to access. Then you have 1 or 
    more levels of cache, which is larger, close by and fast to access. Then you 
    have main memory, which is quite large, but further away so slower to 
    access. Then at the base you have disk acting as virtual memory, which is 
    huge, but very slow.</P>
    <P>Now, if your program is swapping out to disk, you'll realise, because the 
    OS can tell you that it only took 10 seconds of CPU, but 60 seconds elapsed, 
    so you know it spent 50 seconds waiting for disk and that's your speed 
    problem. But if your data is big enough to fit in main RAM, but doesn't all 
    sit in the cache, then the CPU will keep having to wait for data from main 
    RAM. And the OS timers I described count that in the CPU time, so it may not 
    be obvious that memory use is actually your problem.</P></DD></DL>
  <P>This is the original code for the part of the Encode compiler 
  (<code>enc2xs</code>) that generates the warnings on duplicate characters:</P><PRE>    if (exists $seen{$uch}) {
        warn sprintf("U%04X is %02X%02X and %02X%02X\n",
                     $val,$page,$ch,@{$seen{$uch}});
    }
    else {
        $seen{$uch} = [$page,$ch];
    }</PRE>
  <P>It uses the hash <code>%seen</code> to remember all the Unicode characters 
  that it has processed. The first time that it meets a character it won't be in 
  the hash, the <code>exists</code> is false, so the <code>else</code> block 
  executes. It stores an arrayref containing the code page and character number 
  in that page. That's <STRONG>three</STRONG> things per character, and there 
  are a lot of characters in Chinese.</P>
  <P>If it ever sees the same Unicode character again, it prints a warning 
  message. The warning message is just a string, and this is the only place that 
  uses the data in <code>%seen</code>. So I changed the code - I pre-formatted 
  that bit of the error message, and stored a single scalar rather than the 
  three:</P><PRE>    if (exists $seen{$uch}) {
        warn sprintf("U%04X is %02X%02X and %04X\n",
                     $val,$page,$ch,$seen{$uch});
    }
    else {
        $seen{$uch} = $page << 8 | $ch;
    }</PRE>
  <P>That reduced the memory usage by a third, and it runs more 
quickly.</P></DD></DL>
<h2><A name=step_by_step>Step by step</a></h2>
<P>How do you make things faster? Well, this is something of a black art, down 
to trial and error. I'll expand on aspects of these 4 points in the next 
slides.</P>
<DL>
  <DT><a name=item_what_might_be_slow%3f>哪些是慢的？</A> 
  <DD>你必须找到那些使真正慢的东西。在已足够快的代码上浪费精力是不值得的 - 将其放在能获得最大回报的代码上吧。
  <DT><A name=item_think_of_re%2dwrite>考虑重写</A> 
  <DD>不管你怎么咒骂它们，并非所有的代码都能变得快起来。所以剩下真正能加速的是找出另一种能完成同样工作的方法。
  <DT><A name=item_try_it>尝试</A> 
  <DD>但是这或许不可行。检查它是<EM>更快</EM>的和给出了同样的结果。
  <DT><A name=item_note_results>Note results</A> 
  <DD>Either way, note your results - I find a comment in the code is good. It's 
  important if an idea didn't work, because it stops you or anyone else going 
  back and trying the same thing again. And it's important if a change 
  <EM>does</EM> work, as it stops someone else (such as yourself next month) 
  tidying up an important optimisation and losing you that hard won speed gain. 
  <P>By having commented out slower code near the faster code you can look back 
  and get ideas for other places you might optimise in the same way.</P></DD></DL>
<h2><A name=small_easy_things>简单的小事</a></h2>
<P>下面是一些我认为有用的习惯，所以您应当将它们运用到日常程序中。</P>
<DL>
  <DT><a name=item_autosplit_and_autoloader><code>AutoSplit</code> 与 
  <code>AutoLoader</code></A> 
  <DD>如果您在写模块，使用模块 <EM>AutoSplit</EM> 与 <EM>AutoLoader</EM> 可以使 perl 只加载那些特定程序实际所用到的模块部分。如此您能获得两个好处 - 在开始加载时不浪费 CPU 去倒入您不用的模块部分，还有当代码编译时不浪费 RAM 去保持着编译的结构。所以您的模块能加载更快，和使用更少的 RAM 。
  <P>一个可能的问题是使用 AutoLoader 让子程序带来调试混乱。当处于测试状态，您能通过在自动加载子程序前添加注释 <code>__END__</code> 来使 <code>AutoLoader</code> 失效。如此一来，它们就普通地被加载，编译和测试了。</P><PRE>  ...
  1;
  # While debugging, disable AutoLoader like this:
  # __END__
  ...</PRE>
  <P>当然，为了使 <code>use</code> 正常，您还得在加载程序段的后面另添加 <code>1;</code> 和可能要另一个 <code>__END__</code> 。</P>
  <P>Schwern notes that commenting out <code>__END__</code> can cause surprises 
  if the main body of your module is running under <code>use strict;</code> 
  because now your AutoLoaded subroutines will suddenly find themselves being 
  run under <code>use strict</code>. This is arguably a bug in the current 
  <code>AutoSplit</code> - when it runs at install time to generate the files 
  for <code>AutoLoader</code> to use it doesn't add lines such as <code>use 
  strict;</code> or <code>use warnings;</code> to ensure that the split out 
  subroutines are in the same environment as was current at the 
  <code>__END__</code> statement. This may be fixed in 5.10.</P>
  <P>Elizabeth Mattijsen notes that there are different memory use versus memory 
  shared issues when running under <A 
  href="#item_mod_perl"><code>mod_perl</code></A>, 
  with different optimal solutions depending on whether your <code>apache</code> 
  is forking or threaded.</P>
  <DT><A name=item_%3dpod_%40___end__><code>=pod</code> @ 
  <code>__END__</code></A> 
  <DD>如果您用了一大块 pod 来描述你的代码，那么请尽量不要将其放在文件的上面部分。虽然 perl 分析器能很快的跳过 pod ，但是这不是魔法，它还是需要一点时间的。此外，它也需要从磁盘中读入 pod 就为了忽略它。
<PRE>  #!perl -w
  use strict;</PRE><PRE>  =head1 You don't want to do that</PRE><PRE>  big block of pod</PRE><PRE>  =cut</PRE><PRE>  ...
  1;
  __END__</PRE><PRE>  =head1 You want to do this</PRE>
  <P>如果您将您的代码放在 <code>__END__</code> 后面，那么 perl 分析器就不会去注意它。这能省下一点点 CPU，但是如果你有一块很大的 pod (>4K) ，那它意味着文件的最后磁盘<code>块</code>将不会被读进 RAM 。这也许能获得某些加速。[A helpful heckler observed that modern raid systems may well 
  be reading in 64K chunks, and modern OSes are getting good at read ahead, so 
  not reading a block as a result of <code>=pod</code> @ <code>__END__</code> 
  may actually be quite rare.]</P>
  <P>如果你还是将您的 pod （和测试）放在函数代码的旁边（这看起来更是一种好习惯），那么此建议与您无关。</P></DD></DL>
<h2><A name=needless_importing_is_slow>无关的倒入会变慢</a></h2>
<P><code>Exporter</code> 是用 perl 所写的。虽然它很快，但也不是即时的。</P>
<P>许多模块，为了节省您的输入，都默认在您的命名空间内倒出许多函数和符号变量。如果您只有一个参数在 use 后（模块名参数），比如</P><PRE>    use POSIX;          # Exports all the defaults</PRE>
<P>于是 <code>POSIX</code> 将有用地在您的命名空间内倒出它的默认符号变量列表。如果您在模块名后有一列表，那它只倒出此列表的符号变量。如果列表为空， 不到处任何符号变量：</P><PRE>    use POSIX ();       # Exports nothing.</PRE>
<P>您仍然可以使用所有的函数和其他符号变量 - 但您必须使用它们的全名，如在前面输入 <code>POSIX::</code> 。许多人说这样实际上让您的代码更干净，而且现在很清楚的知道子程序是在哪定义的。除了这些，它还更快：</P>
<TABLE cellSpacing=10>
  <TBODY>
  <TR>
    <TD>use POSIX;</TD>
    <TD>use POSIX <B>()</B>;</TD></TR>
  <TR>
    <TD>0.516s</TD>
    <TD>0.355s</TD></TR>
  <TR>
    <TD>use Socket;</TD>
    <TD>use Socket <B>()</B>;</TD></TR>
  <TR>
    <TD>0.270s</TD>
    <TD>0.231s</TD></TR></TBODY></TABLE>
<P><code>POSIX</code> 默认倒出一大堆符号变量。如果您使用了不倒出，它在开始就 <EM>少 30% 的时间</EM>。 <code>Socket</code> 能少 15% 的时间。</P>
<h2><a name=regexps>regexps</a></h2>
<DL>
  <DT><a name=item_avoid_%24%26>avoid $&</A> 
  <DD>The <code>$&</code> variable returns the last text successfully 
  matched in any regular expression. It's not lexically scoped, so unlike the 
  match variables <code>$1</code> etc it isn't reset when you leave a block. 
  This means that to be correct perl has to keep track of it from any match, as 
  perl has no idea when it might be needed. As it involves taking a copy of the 
  matched string, it's expensive for perl to keep track of. If you never mention 
  <code>$&</code>, then perl knows it can cheat and never store it. But if 
  you (<STRONG>or any module</STRONG>) mentions <code>$&</code> anywhere 
  then perl has to keep track of it throughout the script, which slows things 
  down. So it's a good idea to capture the whole match explicitly if that's what 
  you need. <PRE>    $text =~ /.* rules/;
    $line = $&;                 # Now every match will copy $& - slow</PRE><PRE>    $text =~ /(.* rules)/;
    $line = $1;                 # Didn't mention $& - fast</PRE>
  <DT><A name=item_avoid_use_english%3b>avoid use English;</A> 
  <DD><code>use English</code> gives helpful long names to all the punctuation 
  variables. Unfortunately that includes aliasing <code>$&</code> to 
  <code>$MATCH</code> which makes perl think that it needs to copy every match 
  into <code>$&</code>, even if you script never actually uses it. In perl 
  5.8 you can say <code>use English '-no_match_vars';</code> to avoid mentioning 
  the naughty "word", but this isn't available in earlier versions of perl. 
  <DT><A name=item_avoid_needless_captures>avoid needless captures</A> 
  <DD>Are you using parentheses for capturing, or just for grouping? Capturing 
  involves perl copying the matched string into <code>$1</code> etc, so it all 
  you need is grouping use a the non-capturing <code>(?:...)</code> instead of 
  the capturing <code>(...)</code>. 
  <DT><A name=item_%2f%2e%2e%2e%2fo%3b>/.../<STRONG>o</STRONG>;</A> 
  <DD>If you define scalars with building blocks for your regexps, and then make 
  your final regexp by interpolating them, then your final regexp isn't going to 
  change. However, perl doesn't realise this, because it sees that there are 
  interpolated scalars each time it meets your regexp, and has no idea that 
  their contents are the same as before. If your regexp doesn't change, then use 
  the <code>/o</code> flag to tell perl, and it will never waste time checking 
  or recompiling it. 
  <DT><A name=item_but_don%27t_blow_it>but don't blow it</A> 
  <DD>You can use the <code>qr//</code> operator to pre-compile your regexps. It 
  often is the easiest way to write regexp components to build up more complex 
  regexps. Using it to build your regexps once is a good idea. But don't screw 
  up (like parrot's <code>assemble.pl</code> did) by telling perl to recompile 
  the same regexp every time you enter a subroutine: <PRE>    sub foo {
        my $reg1 = qr/.../;
        my $reg2 = qr/... $reg1 .../;</PRE>
  <P>You should pull those two regexp definitions out of the subroutine into 
  package variables, or file scoped lexicals.</P></DD></DL>
<h2><A name=devel::dprof>Devel::DProf</a></h2>
<P>You find what is slow by using a profiler. People often guess where they 
think their program is slow, and get it hopelessly wrong. Use a profiler.</P>
<P><code>Devel::DProf</code> is in the perl core from version 5.6. If you're 
using an earlier perl you can <A 
href="http://search.cpan.org/search?mode=module&query=Devel::DProf">get it 
from CPAN</A>.</P>
<P>You run your program with <code>-d:DProf</code></P><PRE>    perl5.8.0 -d:DProf enc2xs.orig -Q -O -o /dev/null ...</PRE>
<P>which times things and stores the data in a file named <EM>tmon.out</EM>. 
Then you run <code>dprofpp</code> to process the <EM>tmon.out</EM> file, and 
produce meaningful summary information. This excerpt is the default length and 
format, but you can use options to change things - see the man page. It also 
seems to show up a minor bug in <code>dprofpp</code>, because it manages to 
total things up to get 106%. While that's not right, it doesn't affect the 
explanation.</P><PRE>    Total Elapsed Time = 66.85123 Seconds
      User+System Time = 62.35543 Seconds
    Exclusive Times
    %Time ExclSec CumulS #Calls sec/call Csec/c  Name
     106.   66.70 102.59 218881   0.0003 0.0005  main::enter
     49.5   30.86 91.767      6   5.1443 15.294  main::compile_ucm
     19.2   12.01  8.333  45242   0.0003 0.0002  main::encode_U
     4.74   2.953  1.078  45242   0.0001 0.0000  utf8::unicode_to_native
     4.16   2.595  0.718  45242   0.0001 0.0000  utf8::encode
     0.09   0.055  0.054      5   0.0109 0.0108  main::BEGIN
     0.01   0.008  0.008      1   0.0078 0.0078  Getopt::Std::getopts
     0.00   0.000 -0.000      1   0.0000      -  Exporter::import
     0.00   0.000 -0.000      3   0.0000      -  strict::bits
     0.00   0.000 -0.000      1   0.0000      -  strict::import
     0.00   0.000 -0.000      2   0.0000      -  strict::unimport</PRE>
<P>At the top of the list, the subroutine <code>enter</code> takes about half 
the total CPU time, with 200,000 calls, each very fast. That makes it a good 
candidate to optimise, because all you have to do is make a slight change that 
gives a small speedup, and that gain will be magnified 200,000 times. [It turned 
out that <code>enter</code> was tail recursive, and part of the speed gain I got 
was by making it loop instead]</P>
<P>Third on the list is <code>encode_U</code>, which with 45,000 calls is 
similar, and worth looking at. [Actually, it was trivial code and in the real 
<code>enc2xs</code> I inlined it]</P>
<P><code>utf8::unicode_to_native</code> and <code>utf8::encode</code> are 
built-ins, so you won't be able to change that.</P>
<P>Don't bother below there, as you've accounted for 90% of total program time, 
so even if you did a perfect job on everything else, you could only make the 
program run 10% faster.</P>
<P><code>compile_ucm</code> is trickier - it's only called 6 times, so it's not 
obvious where to look for what's slow. Maybe there's a loop with many 
iterations. But now you're guessing, which isn't good.</P>
<P>One trick is to break it into several subroutines, just for benchmarking, so 
that DProf gives you times for different bits. That way you can see where the 
juicy bits to optimise are.</P>
<P><code>Devel::SmallProf</code> should do line by line profiling, but every 
time I use it it seems to crash.</P>
<h2><a name=benchmark>Benchmark</a></h2>
<P>现在您已经确定了慢的地方，您还需要在两个代码中找到更快的一个。模块 <code>Benchmark</code> 能让这变得容易。一个特别好的子程序是 <code>cmpthese</code> ，它能摘录代码片段而且绘制图表。<code>cmpthese</code> 可于 perl 5.6 的 <code>Benchmark</code> 里发现。</P>
<P>因为为了比较各运行 1000 次的两个代码片段 <code>orig</code> 和 <code>new</code> ，您可以这么做</P><PRE>    use Benchmark ':all';
    
    sub orig {
       ...
    }
    
    sub new {
       ...
    }
    
    cmpthese (10000, { orig => \&orig, new => \&new } );</PRE>
<P>Benchmark 运行了两者，分别计时，然后输出一个有用的对比图表：</P><PRE>    Benchmark: timing 10000 iterations of new, orig...
           new:  1 wallclock secs ( 0.70 usr +  0.00 sys =  0.70 CPU) @ 14222.22/s (n=10000)
          orig:  4 wallclock secs ( 3.94 usr +  0.00 sys =  3.94 CPU) @ 2539.68/s (n=10000)
            Rate orig  new
    orig  2540/s   -- -82%
    new  14222/s 460%   --</PRE>
<P>很容易看清楚新的代码比原始代码快 4 倍。</P>
<h2><a name=what_causes_slowness_in_perl>What causes slowness in perl?</a></h2>
<P>Actually, I didn't tell the whole truth earlier about what causes slowness in 
perl. [And astute hecklers such as Philip Newton had already told me this]</P>
<P>When perl compilers your program it breaks it down into a sequence of 
operations it must perform, which are usually referred to as <EM>ops</EM>. So 
when you ask perl to compute <code>$a = $b + $c</code> it actually breaks it 
down into these ops:</P>
<UL>
  <LI><a name=item_fetch_%24b_onto_the_stack>Fetch <code>$b</code> onto the 
  stack</A> 
  <LI><A name=item_fetch_%24c_onto_the_stack>Fetch <code>$c</code> onto the 
  stack</A> 
  <LI><A name=item_add_the_top_two_things_on_the_stack_together%3b_wr>Add the 
  top two things on the stack together; write the result to the stack</A> 
  <LI><A name=item_fetch_the_address_of_%24a>Fetch the address of 
  <code>$a</code></A> 
  <LI><A name=item_place_the_thing_on_the_top_of_stack_into_that_addr>Place the 
  thing on the top of stack into that address</A> </LI></UL>
<P>Computers are fast at simple things like addition. But there is quite a lot 
of overhead involved in keeping track of "which op am I currently performing" 
and "where is the next op", and this book-keeping often swamps the time taken to 
actually run the ops. So often in perl it's the number of ops your program takes 
to perform its task that is more important than the CPU they use or the RAM it 
needs. The hit list is</P>
<OL>
  <LI>Ops 
  <LI>CPU 
  <LI>RAM </LI></OL>
<P>So what were my example code snippets that I <code>Benchmark</code>ed?</P>
<P>It was code to split a line of hex 
(<code>54726164696e67207374796c652f6d61</code>) into groups of 4 digits 
(<code>5472 6164 696e</code> ...) , and convert each to a number</P><PRE>    sub orig {
       map {hex $_} $line =~ /(....)/g;
    }</PRE><PRE>    sub new {
       unpack "n*", pack "H*", $line;
    }</PRE>
<P>The two produce the same results:</P>
<HR>

<TABLE>
  <TBODY>
  <TR>
    <TH>orig</TH>
    <TH>new</TH></TR>
  <TR>
    <TD>21618, 24932, 26990, 26400, 29556, 31084, 25903, 28001, 26990, 29793, 
      26990, 24930, 26988, 26996, 31008, 26223, 29216, 29552, 25957, 25646</TD>
    <TD>21618, 24932, 26990, 26400, 29556, 31084, 25903, 28001, 26990, 29793, 
      26990, 24930, 26988, 26996, 31008, 26223, 29216, 29552, 25957, 
  25646</TD></TR></TBODY></TABLE>
<P>but the first one is much slower. Why? Following the data path from right to 
left, it starts well with a global regexp, which is only one op and therefore a 
fast way to generate a list of the 4 digit groups. But that <code>map</code> 
block is actually an implicit loop, so for each 4 digit block it iterates round 
and repeatedly calls <code>hex</code>. Thats at least one op for every list 
item.</P>
<P>Whereas the second one has no loops in it, implicit or explicit. It uses one 
<code>pack</code> to convert the hex temporarily into a binary string, and then 
one <code>unpack</code> to convert that string into a list of numbers. 
<code>n</code> is big endian 16 bit quantities. I didn't know that - I had to 
look it up. But when the profiler told me that this part of the original code 
was a performance bottleneck, the first think that I did was to look at the the 
pack docs to see if I could use some sort of 
<code>pack</code>/<code>unpack</code> as a speedier replacement.</P>
<h2><A name="ops_are_bad,_m'kay1">Ops are bad, m'kay</a></h2>
<P>You can ask perl to tell you the ops that it generates for particular code 
with the <code>Terse</code> backend to the compiler. For example, here's a 1 
liner to show the ops in the original code:</P>
<P><code>$ perl -MO=Terse -e'map {hex $_} $line =~ /(....)/g;'</code></P><PRE>    LISTOP (0x16d9c8) leave [1]
        OP (0x16d9f0) enter
        COP (0x16d988) nextstate
        LOGOP (0x16d940) mapwhile [2]
            LISTOP (0x16d8f8) mapstart
                OP (0x16d920) pushmark
                UNOP (0x16d968) null
                    UNOP (0x16d7e0) null
                        LISTOP (0x115370) scope
                            OP (0x16bb40) null [174]
                            UNOP (0x16d6e0) hex [1]
                                UNOP (0x16d6c0) null [15]
                                    SVOP (0x10e6b8) gvsv  GV (0xf4224) *_
                PMOP (0x114b28) match /(....)/
                    UNOP (0x16d7b0) null [15]
                        SVOP (0x16d700) gvsv  GV (0x111f10) *line</PRE>
<P>At the bottom you can see how the match <code>/(....)/</code> is just one op. 
But the next diagonal line of ops from <code>mapwhile</code> down to the match 
are all the ops that make up the <code>map</code>. Lots of them. <EM>And</EM> 
they get run <STRONG>each time round map's loop</STRONG>. [Note also that the 
<code>{}</code>s mean that map enters scope each time round the loop. That not a 
trivially cheap op either]</P>
<P>Whereas my replacement code looks like this:</P>
<P><code>$ perl -MO=Terse -e'unpack "n*", pack "H*", $line;'</code></P><PRE>    LISTOP (0x16d818) leave [1]
        OP (0x16d840) enter
        COP (0x16bb40) nextstate
        LISTOP (0x16d7d0) unpack
            OP (0x16d7f8) null [3]
            SVOP (0x10e6b8) const  PV (0x111f94) "n*"
            LISTOP (0x115370) pack [1]
                OP (0x16d7b0) pushmark
                SVOP (0x16d6c0) const  PV (0x111f10) "H*"
                UNOP (0x16d790) null [15]
                    SVOP (0x16d6e0) gvsv  GV (0x111f34) *line</PRE>
<P>There are less ops in total. And no loops, so all the ops you see execute 
only once. :-)</P>
<P>[My helpful hecklers pointed out that it's hard to work out what an op is. 
Good call. There's roughly one op per symbol (function, operator, variable name, 
and any other bit of perl syntax). So if you golf down the number of functions 
and operators your program runs, then you'll be reducing the number of ops.]</P>
<P>[These were supposed to be the bonus slides. I talked to fast (quelle 
surprise) and so manage to actually get through the lot with time for 
questions]</P>
<h2><a name=memoize>Memoize</a></h2>
<DL>
  <DT><a name=item_caches_function_results>Caches function results</A> 
  <DD>MJD's <code>Memoize</code> follows the grand perl tradition by trading 
  memory for speed. You tell <code>Memoize</code> the name(s) of functions you'd 
  like to speed up, and it does symbol table games to transparently intercept 
  calls to them. It looks at the parameters the function was called with, and 
  uses them to decide what to do next. If it hasn't seen a particular set of 
  parameters before, it calls the original function with the parameters. 
  However, before returning the result, it stores it in a hash for that 
  function, keyed by the function's parameters. If it has seen the parameters 
  before, then it just returns the result direct from the hash, without even 
  bothering to call the function. 
  <DT><A name=item_for_functions_that_only_calculate>For functions that only 
  calculate</A> 
  <DD>This is useful for functions that calculate things with no side effects, 
  slow functions that you often call repeatedly with the same parameters. It's 
  not useful for functions that do things external to the program (such as 
  generating output), nor is it good for very small, fast functions. 
  <DT><A name=item_can_tie_cache_to_a_disk_file>Can tie cache to a disk file</A> 

  <DD>The hash <code>Memoize</code> uses is a regular perl hash. This means that 
  you can tie the hash to a disk file. This allows <code>Memoize</code> to 
  remember things across runs of your program. That way, you could use 
  <code>Memoize</code> in a CGI to cache static content that you only generate 
  on demand (but remember you'll need file locking). The first person who 
  requests something has to wait for the generation routine, but everyone else 
  gets it straight from the cache. You can also arrange for another program to 
  periodically expire results from the cache. </DD></DL>
<P>As of 5.8 <code>Memoize</code> module has been assimilated into the core. 
Users of earlier perl can <A 
href="http://search.cpan.org/search?mode=module&query=Memoize">get it from 
CPAN</A>.</P>
<h2><A name=miscellaneous>Miscellaneous</a></h2>
<P>These are quite general ideas for optimisation that aren't particularly perl 
specific.</P>
<DL>
  <DT><a name=item_pull_things_out_of_loops>Pull things out of loops</A> 
  <DD>perl's hash lookups are fast. But they aren't as fast as a lexical 
  variable. <code>enc2xs</code> was calling a function each time round a loop 
  based on a hash lookup using <code>$type</code> as the key. The value of 
  <code>$type</code> didn't change, so I pulled the lookup out above the loop 
  into a lexical variable: <PRE>    my $type_func = $encode_types{$type};</PRE>
  <P>and doing it only once was faster.</P>
  <DT><A name=item_experiment_with_number_of_arguments>Experiment with number of 
  arguments</A> 
  <DD>Something else I found was that <code>enc2xs</code> was calling a function 
  which took several arguments from a small number of places. The function 
  contained code to set defaults if some of the arguments were not supplied. I 
  found that the way the program ran, most of the calls passed in all the values 
  and didn't need the defaults. Changing the function to not set defaults, and 
  writing those defaults out explicitly where needed bought me a speed up. 
  <DT><A name=item_tail_recursion>Tail recursion</A> 
  <DD>Tail recursion is where the last thing a function does it call itself 
  again with slightly different arguments. It's a common idiom, and some 
  languages can automatically optimise it away. Perl is not one of those 
  languages. So every time a function tail recurses you have another subroutine 
  call [not cheap - Arthur Bergman notes that it is 10 pages of C source, and 
  will blow the instruction cache on a CPU] and re-entering that subroutine 
  again causes more memory to be allocated to store a new set of lexical 
  variables [also not cheap]. 
  <P>perl can't spot that it could just throw away the old lexicals and re-use 
  their space, but <EM>you</EM> can, so you can save CPU and RAM by re-writing 
  your tail recursive subroutines with loops. In general, trying to reduce 
  recursion by replacing it with iterative algorithms should speed things 
  up.</P></DD></DL>
<h2><A name=yay_for_y>yay for y</a></h2>
<P><code>y</code>, or <code>tr</code>, is the transliteration operator. It's not 
as powerful as the general purpose regular expression engine, but for the things 
it can do it is often faster.</P>
<DL>
  <DT><a name=item_tr%2f%21%2f%2f_%23_fastest_way_to_count_chars>tr/!// # 计算字符次数的更快的方法</A> 
  <DD><code>tr</code> 并不删除字符除非你使用了 <code>/d</code> 。如果你没有任何代替字符操作，它使用了只读。在标量上下文中，它返回匹配字符的次数。这是一种最快速的方法来计算单字符或字符串被匹配的次数。（它比在列表上下文上使用 <code>m/.../g</code> 要快。但是如果你只是检查某字符是否出现一次以上，那请使用 <code>m/.../</code> ，因为它会在第一次检查到就停止，而 <code>tr///</code> 会检查到最后）
  <DT><A name=item_tr/>tr/q/Q/ 比 s/q/Q/g 更快</A> 
  <DD><code>tr</code> is also faster than the regexp engine for doing 
  character-for-character substitutions. 
  <DT>tr/a-z//d 比 s/[a-z]//g 更快
  <DD><code>tr</code> is faster than the regexp engines for doing character 
  range deletions. [When writing the slide I assumed that it would be faster for 
  single character deletions, but I <code>Benchmark</code>ed things and found 
  that <code>s///g</code> was faster for them. So never guess timings; always 
  test things. You'll be surprised, but that's better than being wrong] </DD></DL>
<h2><A name="ops_are_bad,_m'kay2">Ops are bad, m'kay</a></h2>
<P>Another example lifted straight from <code>enc2xs</code> of something that I 
managed to accelerate quite a bit by reducing the number of ops run. The code 
takes a scalar, and prints out each byte as \x followed by 2 digits of hex, as 
it's generating C source code:</P><PRE>    #foreach my $c (split(//,$out_bytes)) {
    #  $s .= sprintf "\\x%02X",ord($c);
    #}
    # 9.5% faster changing that loop to this:
    $s .= sprintf +("\\x%02X" x length $out_bytes), unpack "C*", $out_bytes;</PRE>
<P>The original makes a temporary list with <code>split</code> [not bad in 
itself - ops are more important than CPU or RAM] and then loops over it. Each 
time round the loop it executes several ops, including using ord to convert the 
byte to its numeric value, and then using sprintf with the format 
<code>"\\x%02X"</code> to convert that number to the C source.</P>
<P>The new code effectively merges the <code>split</code> and looped 
<code>ord</code> into one op, using <code>unpack</code>'s <code>C</code> format 
to generate the list of numeric values directly. The more interesting (arguably 
sick) part is the format to <code>sprintf</code>, which is inside 
<code>+(...)</code>. You can see from the <code>.=</code> in the original that 
the code is just concatenating the converted form of each byte together. So 
instead of making <code>sprintf</code> convert each value in turn, only for perl 
ops to stick them together, I use <code>x</code> to replicate the per-byte 
format string once for each byte I'm about to convert. There's now one 
<code>"\\x%02X"</code> for each of the numbers in the list passed from 
<code>unpack</code> to <code>sprintf</code>, so sprintf just does what it's 
told. And <code>sprintf</code> is faster than perl ops.</P>
<h2><a name=how_to_make_perl_fast_enough>How to make perl fast enough</a></h2>
<DL>
  <DT><A name=item_use_the_language%27s_fast_features>use the language's fast 
  features</A> 
  <DD>You have enormous power at your disposal with regexps, <code>pack</code>, 
  <code>unpack</code> and <code>sprintf</code>. So why not use them? 
  <P>All the <code>pack</code> and <code>unpack</code> code is implemented in 
  pure C, so doesn't have any of the book-keeping overhead of perl ops. 
  <code>sprintf</code> too is pure C, so it's fast. The regexp engine uses its 
  own private bytecode, but it's specially tuned for regexps, so it runs much 
  faster than general perl code. And the implementation of <code>tr</code> has 
  less to do than the regexp engine, so it's faster.</P>
  <P>For maximum power, remember that you can generate regexps and the formats 
  for <code>pack</code>, <code>unpack</code> and <code>sprintf</code> at run 
  time, based on your data.</P>
  <DT><A name=item_give_the_interpreter_hints>give the interpreter hints</A> 
  <DD>Make it obvious to the interpreter what you're up to. Avoid 
  <code>$&</code>, use <code>(?:...)</code> when you don't need capturing, 
  and put the <code>/o</code> flag on constant regexps. 
  <DT><A name=item_less_ops>less OPs</A> 
  <DD>Try to accomplish your tasks using less operations. If you find you have 
  to optimise an existing program then this is where to start - golf is good, 
  but remember it's run time strokes not source code strokes. 
  <DT><A name=item_less>less CPU</A> 
  <DD>Usually you want to find ways of using less CPU. 
  <DT>less RAM 
  <DD>but don't forget to think about how your data structures work to see if 
  you can make them use less RAM. </DD></DL></div></div>
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